Vector-based seismic decomposition by reverse time methods

IF 0.7 Q4 GEOSCIENCES, MULTIDISCIPLINARY Russian Journal of Earth Sciences Pub Date : 2023-08-13 DOI:10.2205/2023es000837
Vadim Agafonov, Aleksandr Bugaev, Gennadiy Erokhin, Andrey Ronzhin
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Abstract

The paper analyzes the stage of decomposition of the initial seismic data in the methods of wave reversal in time when constructing seismic attributes. Within the framework of the formal approach of mapping the data of one space into the data of a space of a higher dimension, a classification of existing approaches in seismic exploration is given. Identification of the decomposition stage in the seismic data processing workflow makes it possible to highlight the differences in existing approaches to building seismic attributes and predict the future direction of seismic data processing. The concept of vector decomposition, originally used in the RTH method, is introduced. The variety of depth seismic attributes obtained in the RTH method based on vector decomposition allows solving a wide range of problems in the exploration and development of hydrocarbon deposits at a new qualitative level. The RTH method includes, as a special case, the PSDM, AVO, AI methods and is an alternative to the MVA, FWI methods, as well as the method of a velocity model bilding based on fast beam migration algorithms. A close connection between the technique of wavefront time reversal in seismic exploration and analogous time reversal in optics and acoustics is noted. Examples of seismic data processing using vector decompositio to identify zones of natural fracturing in shale oil are given.
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基于矢量的逆时地震分解方法
本文分析了在构造地震属性时,及时波反演方法中初始地震资料的分解阶段。在将一个空间的数据映射到更高维度空间数据的形式化方法框架内,对现有的地震勘探方法进行了分类。识别地震数据处理流程中的分解阶段,可以突出现有地震属性构建方法的差异,并预测地震数据处理的未来方向。引入了原用于RTH方法的矢量分解的概念。基于矢量分解的RTH方法所获得的深度地震属性的多样性,使油气勘探开发中的一系列问题在一个新的定性水平上得到了解决。作为特例,RTH方法包括PSDM、AVO、AI方法,是MVA、FWI方法以及基于快速波束偏移算法的速度模型建立方法的替代方法。指出了地震勘探中的波前时间反演技术与光学和声学中的类似时间反演技术之间的密切联系。给出了用矢量分解方法识别页岩油天然压裂带的地震数据处理实例。
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来源期刊
Russian Journal of Earth Sciences
Russian Journal of Earth Sciences GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
1.90
自引率
15.40%
发文量
41
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